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Article
Mathematics, Applied
Hao Chen et al.
Summary: Research reveals that inter-network combat and intra-network cooperation among structured systems have likely been recurring themes in human evolutionary history. However, little is known about the combat mechanism between structured systems, where adversarial interactions lead to agent disability and a tendency to seek cooperation with neighbors. This study proposes a two-network combat game model and corresponding rules for attack, disability, cooperation, and winning.
Article
Automation & Control Systems
Wenying Xu et al.
Summary: This article focuses on the problem of hybrid Nash equilibrium seeking over a network in a partial-decision information scenario. A fully distributed adaptive gradient-based algorithm is constructed with guaranteed convergence to the equilibrium. To save communication cost, a novel event-triggered scheme called edge-based adaptive dynamic event-triggered (E-ADET) scheme is proposed, which is proven to be fully distributed and free of Zeno behavior. Furthermore, a fully distributed hybrid equilibrium seeking algorithm is constructed under the E-ADET scheme, with convergence guaranteed by utilizing the Lipschitz continuity and the strong monotonicity of the pseudogradient mapping.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Computer Science, Artificial Intelligence
Wen-Ting Lin et al.
Summary: This paper proposes a distributed optimization algorithm for aggregative game with coupled constraints. The algorithm seeks the generalized Nash equilibrium based on the singular perturbation system by using the average consensus method in the fast manifold. The estimation of aggregates and dual variable aggregates is achieved through simple information exchanges, providing necessary information for the fully distributed algorithm design. The exponential convergence of the algorithm is explored based on the Lyapunov method, the properties of the variational inequality, and the characteristic of the singular perturbation system. The effectiveness of the proposed algorithm is verified through its application to the resource competition problem in smart grid.
Article
Mathematics, Applied
Qirui Yang et al.
Summary: This paper studies the evolution process of competitive dynamics on triplex complex networks. A new triplex network model is proposed, where the state of each layer's node is influenced by both its neighbors and inter-layer competition. Through numerical simulations, the influence of various factors on the evolution process and competitiveness of the network is discussed. Specific suggestions for improving the competitiveness of the platform in reality are given based on simulation results.
Article
Engineering, Chemical
Jiachen Liu et al.
Summary: The energy-minimization multiscale (EMMS) model explores the compromise between dominant mechanisms in gas-solid and gas-liquid systems using stability conditions. This study utilizes game theory and noninferior solutions to solve the multi-objective variational problem and investigates the interactions between different dominant mechanisms in flow regime transitions.
CHINESE JOURNAL OF CHEMICAL ENGINEERING
(2022)
Article
Automation & Control Systems
Wenying Xu et al.
Summary: This article investigates the issue of second-order consensus for multiagent systems under limited communication resources and replay attacks. By proposing an asynchronous dynamic edge event-triggered communication scheme and an attack-resilient consensus protocol, it successfully addresses the impact caused by replay attacks.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Automation & Control Systems
Deming Yuan et al.
Summary: This article discusses an online distributed composite optimization problem and proposes a class of algorithms based on approximate mirror descent. These algorithms are effective in handling composite optimization problems with loss functions and regularization functions under different information feedback models.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2021)
Article
Multidisciplinary Sciences
Cecilia Lindig-Leon et al.
Summary: Different learning algorithms are crucial for understanding human sensorimotor interactions, as game-theoretic analysis alone focusing on the Nash equilibrium concept may not capture the full dynamics. Q-learning with intrinsic costs is shown to best explain the observed data in this study.
SCIENTIFIC REPORTS
(2021)
Article
Automation & Control Systems
Kaihong Lu et al.
Summary: This article explores the distributed generalized Nash equilibrium seeking problem in noncooperative games in dynamic environments. An online distributed algorithm based on consensus algorithms and a primal-dual strategy is proposed to address the issue. The effectiveness of the theoretical results is demonstrated through a simulation.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2021)
Article
Engineering, Civil
Peng Hang et al.
Summary: This paper presents a human-like decision making framework for AVs considering the coexistence of human-driven vehicles and autonomous vehicles in the future. Different driving styles, social interaction characteristics, game theory, and model predictive control are applied for decision making in AVs. Testing scenarios of lane change show that game theoretic approaches can provide reasonable human-like decision making, with the Stackelberg game theory approach reducing the cost value by over 20% under normal driving style compared to the Nash equilibrium approach.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Mathematics, Interdisciplinary Applications
Zhangcheng Feng et al.
Summary: This paper investigates the distributed computation issue of generalized Nash equilibrium (GNE) in a multi-player game with shared coupling constraints. Two relatively fast distributed algorithms are proposed, and their convergence to GNE with fixed step-sizes is proven under the assumptions of Lipschitz continuity. A numerical simulation is provided to demonstrate the efficiency and performance of the algorithm.
FRACTAL AND FRACTIONAL
(2021)
Article
Automation & Control Systems
Mattia Bianchi et al.
Summary: This study presents a distributed algorithm for learning Nash equilibria in time-varying communication networks under partial-decision information scenarios. The algorithm demonstrates linear convergence, outperforming existing gradient-based methods, and allowing for time-varying communication with tighter bounds on convergence. Additionally, a pseudo-gradient algorithm is proposed which guarantees convergence on time-varying balanced directed graphs, relaxing assumptions on network structures.
IEEE CONTROL SYSTEMS LETTERS
(2021)
Article
Automation & Control Systems
Lacra Pavel
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2020)
Article
Engineering, Electrical & Electronic
Xinlei Yi et al.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2020)
Article
Automation & Control Systems
Shaofu Yang et al.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2019)
Article
Automation & Control Systems
Farzad Salehisadaghiani et al.
Article
Automation & Control Systems
Shahin Shahrampour et al.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2018)
Article
Computer Science, Information Systems
Danilo Ardagna et al.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2017)
Article
Automation & Control Systems
Farzad Salehisadaghiani et al.
Article
Automation & Control Systems
Ankur A. Kulkarni et al.
Article
Computer Science, Theory & Methods
Arnaud Casteigts et al.
INTERNATIONAL JOURNAL OF PARALLEL EMERGENT AND DISTRIBUTED SYSTEMS
(2012)
Review
Operations Research & Management Science
Francisco Facchinei et al.
ANNALS OF OPERATIONS RESEARCH
(2010)
Article
Engineering, Electrical & Electronic
Michael Maskery et al.
IEEE TRANSACTIONS ON COMMUNICATIONS
(2009)
Article
Mathematics, Applied
Angelia Nedic et al.
SIAM JOURNAL ON OPTIMIZATION
(2009)
Article
Automation & Control Systems
Yiguang Hong et al.
Article
Operations Research & Management Science
A Beck et al.
OPERATIONS RESEARCH LETTERS
(2003)
